{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch \n",
    "import torchvision\n",
    "from torchvision.transforms import ToTensor, Normalize, Compose\n",
    "from torchvision.datasets import MNIST\n",
    "import cv2\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import torch.nn as nn\n",
    "import os\n",
    "from IPython.display import Image\n",
    "from torchvision.utils import save_image\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist = MNIST(root = 'Trainig_data/',\n",
    "             train = True,\n",
    "             download = True, \n",
    "             transform = Compose([ToTensor(), Normalize(mean=(0.5), std = (0.5,))]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def denorm(x):\n",
    "    out = (x+1) / 2\n",
    "    return out.clamp(0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Device Configuring for better performance\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "img, label = mnist[0] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UsageError: unrecognized arguments: # this is magic command\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline # this is magic command\n",
    "i = denorm(img) \n",
    "plt.imshow(i[0], cmap = 'gray')\n",
    "print('Label: ', label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label:  5\n",
      "tensor([[[-0.9922,  0.2078,  0.9843, -0.2941, -1.0000],\n",
      "         [-1.0000,  0.0902,  0.9843,  0.4902, -0.9843],\n",
      "         [-1.0000, -0.9137,  0.4902,  0.9843, -0.4510],\n",
      "         [-1.0000, -1.0000, -0.7255,  0.8902,  0.7647],\n",
      "         [-1.0000, -1.0000, -1.0000, -0.3647,  0.8824]]])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(tensor(-1.), tensor(1.))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img, label = mnist[0]\n",
    "print('Label: ', label)\n",
    "print(img[:,10:15,10:15])\n",
    "torch.min(img), torch.max(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "\n",
    "batch_size = 100\n",
    "data_loader = DataLoader(mnist, batch_size, shuffle = True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "batch size is some thing that is we are taking batches of 100 each so total batches will be 70000/100 i.e 700"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50th batch\n",
      "torch.Size([1, 28, 28])\n",
      "tensor([5, 9, 0, 2, 1, 8, 5, 1, 0, 1, 3, 1, 5, 8, 9, 7, 9, 8, 0, 3, 0, 3, 6, 0,\n",
      "        4, 1, 0, 8, 1, 0, 9, 9, 0, 3, 9, 0, 9, 7, 2, 1, 9, 5, 6, 0, 0, 4, 8, 4,\n",
      "        2, 9, 6, 4, 4, 4, 2, 1, 4, 4, 7, 3, 2, 1, 4, 9, 2, 2, 8, 9, 4, 6, 4, 1,\n",
      "        8, 0, 1, 3, 5, 5, 6, 8, 9, 7, 8, 9, 8, 1, 9, 1, 3, 6, 6, 3, 7, 2, 7, 2,\n",
      "        7, 4, 1, 7])\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for img_batch, label_batch in data_loader:\n",
    "    print('50th batch')\n",
    "    print(img_batch[50].shape)\n",
    "    plt.imshow(img_batch[50][0], cmap = 'gray')\n",
    "    print( label_batch )\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_size = 784\n",
    "hidden_size = 512"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "D = nn.Sequential(\n",
    "    nn.Linear(img_size, hidden_size),\n",
    "    nn.LeakyReLU(0.2),\n",
    "    nn.Linear(hidden_size, hidden_size),\n",
    "    nn.LeakyReLU(0.2),\n",
    "    nn.Linear(hidden_size, 1),\n",
    "    nn.Sigmoid())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (0): Linear(in_features=784, out_features=512, bias=True)\n",
       "  (1): LeakyReLU(negative_slope=0.2)\n",
       "  (2): Linear(in_features=512, out_features=512, bias=True)\n",
       "  (3): LeakyReLU(negative_slope=0.2)\n",
       "  (4): Linear(in_features=512, out_features=1, bias=True)\n",
       "  (5): Sigmoid()\n",
       ")"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "latent_size = 64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = nn.Sequential(\n",
    "    nn.Linear(latent_size, hidden_size),\n",
    "    nn.ReLU(),\n",
    "    nn.Linear(hidden_size, hidden_size),\n",
    "    nn.ReLU(),\n",
    "    nn.Linear(hidden_size, img_size),\n",
    "    nn.Tanh())\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (0): Linear(in_features=64, out_features=512, bias=True)\n",
       "  (1): ReLU()\n",
       "  (2): Linear(in_features=512, out_features=512, bias=True)\n",
       "  (3): ReLU()\n",
       "  (4): Linear(in_features=512, out_features=784, bias=True)\n",
       "  (5): Tanh()\n",
       ")"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.BCELoss()\n",
    "d_optimizer = torch.optim.Adam(D.parameters(),lr = 0.0002)\n",
    "g_optimizer = torch.optim.Adam(G.parameters(),lr = 0.0002)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def reset_grad():\n",
    "    d_optimizer.zero_grad()\n",
    "    g_optimizer.zero_grad()\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_discriminator(img):\n",
    "    # Created the labels which are later used as input for the BCE loss\n",
    "    real_labels = torch.ones(batch_size, 1).to(device)\n",
    "    fake_labels = torch.zeros(batch_size, 1).to(device)\n",
    "    \n",
    "    #loss for real images\n",
    "    outputs = D(img)\n",
    "    d_loss_real = criterion(outputs, real_labels)\n",
    "    real_score = outputs\n",
    "    \n",
    "    #loss for fake images\n",
    "    z = torch.randn(batch_size, latent_size).to(device)\n",
    "    fake_images = G(z)\n",
    "    outputs = D(fake_images)\n",
    "    d_loss_fake = criterion(outputs, fake_labels)\n",
    "    fake_scores = outputs\n",
    "    \n",
    "    #combine losses\n",
    "    d_loss = d_loss_real + d_loss_fake\n",
    "    \n",
    "    #Reset Gradients\n",
    "    reset_grad()\n",
    "    \n",
    "    # Compute gradients\n",
    "    d_loss.backward()\n",
    "    \n",
    "    #Adjust the parameters using backprop\n",
    "    d_optimizer.step()\n",
    "    \n",
    "    return d_loss, real_score, fake_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.4066, 0.5514, 0.4893,  ..., 0.3546, 0.5962, 0.5029],\n",
      "        [0.4118, 0.5783, 0.5105,  ..., 0.4424, 0.5510, 0.5082],\n",
      "        [0.3717, 0.4553, 0.5442,  ..., 0.4306, 0.5032, 0.4814],\n",
      "        ...,\n",
      "        [0.4390, 0.4857, 0.5228,  ..., 0.4536, 0.5346, 0.4648],\n",
      "        [0.4237, 0.4389, 0.4591,  ..., 0.4782, 0.5697, 0.4755],\n",
      "        [0.3917, 0.4710, 0.5033,  ..., 0.4923, 0.5581, 0.4577]],\n",
      "       device='cuda:0', grad_fn=<ClampBackward>)\n",
      "tensor([[0.4376, 0.4871, 0.4905,  ..., 0.4052, 0.5567, 0.5548],\n",
      "        [0.4487, 0.4559, 0.4854,  ..., 0.4152, 0.5862, 0.4363],\n",
      "        [0.4634, 0.4754, 0.4764,  ..., 0.3772, 0.5839, 0.5075],\n",
      "        ...,\n",
      "        [0.4309, 0.5347, 0.4805,  ..., 0.4561, 0.5300, 0.5209],\n",
      "        [0.4235, 0.4489, 0.5307,  ..., 0.4315, 0.5577, 0.4410],\n",
      "        [0.3682, 0.4817, 0.4937,  ..., 0.4697, 0.5484, 0.4415]],\n",
      "       device='cuda:0', grad_fn=<ClampBackward>)\n",
      "tensor([[0.4395, 0.4872, 0.4740,  ..., 0.4806, 0.5384, 0.5432],\n",
      "        [0.4576, 0.4536, 0.5217,  ..., 0.3864, 0.6123, 0.5101],\n",
      "        [0.4713, 0.5003, 0.4447,  ..., 0.4189, 0.6143, 0.5169],\n",
      "        ...,\n",
      "        [0.3985, 0.5405, 0.5013,  ..., 0.4220, 0.5534, 0.4839],\n",
      "        [0.4492, 0.4552, 0.4955,  ..., 0.4649, 0.5600, 0.5109],\n",
      "        [0.5115, 0.5318, 0.4954,  ..., 0.4314, 0.5377, 0.5204]],\n",
      "       device='cuda:0', grad_fn=<ClampBackward>)\n",
      "tensor([[0.5043, 0.5320, 0.4454,  ..., 0.4329, 0.5997, 0.5210],\n",
      "        [0.4509, 0.5187, 0.5149,  ..., 0.4326, 0.5519, 0.4717],\n",
      "        [0.4541, 0.4953, 0.5058,  ..., 0.5191, 0.5905, 0.4162],\n",
      "        ...,\n",
      "        [0.4502, 0.5030, 0.4452,  ..., 0.4714, 0.5626, 0.4965],\n",
      "        [0.4999, 0.4744, 0.5179,  ..., 0.4424, 0.5737, 0.5428],\n",
      "        [0.3832, 0.4976, 0.5220,  ..., 0.4580, 0.5965, 0.4875]],\n",
      "       device='cuda:0', grad_fn=<ClampBackward>)\n",
      "tensor([[0.4755, 0.4922, 0.4817,  ..., 0.4464, 0.5568, 0.4728],\n",
      "        [0.4204, 0.5129, 0.4817,  ..., 0.4962, 0.5767, 0.4737],\n",
      "        [0.4462, 0.4704, 0.5375,  ..., 0.3937, 0.5803, 0.4028],\n",
      "        ...,\n",
      "        [0.4447, 0.5217, 0.4591,  ..., 0.4203, 0.5636, 0.4692],\n",
      "        [0.4862, 0.4674, 0.4532,  ..., 0.5247, 0.5699, 0.5327],\n",
      "        [0.3360, 0.5412, 0.4578,  ..., 0.4323, 0.5026, 0.5155]],\n",
      "       device='cuda:0', grad_fn=<ClampBackward>)\n",
      "tensor([[0.4814, 0.4318, 0.5152,  ..., 0.4909, 0.5499, 0.5167],\n",
      "        [0.4635, 0.4582, 0.5079,  ..., 0.4335, 0.5748, 0.4701],\n",
      "        [0.4551, 0.4840, 0.4911,  ..., 0.4350, 0.5729, 0.5600],\n",
      "        ...,\n",
      "        [0.4043, 0.5004, 0.5225,  ..., 0.4792, 0.5860, 0.4735],\n",
      "        [0.4576, 0.3952, 0.5197,  ..., 0.4088, 0.6279, 0.4794],\n",
      "        [0.3831, 0.4719, 0.4944,  ..., 0.3911, 0.5698, 0.5126]],\n",
      "       device='cuda:0', grad_fn=<ClampBackward>)\n",
      "tensor([[0.4207, 0.4517, 0.4905,  ..., 0.4253, 0.5334, 0.5002],\n",
      "        [0.4175, 0.5017, 0.5007,  ..., 0.4995, 0.5597, 0.4698],\n",
      "        [0.4046, 0.4861, 0.5075,  ..., 0.4227, 0.5331, 0.4745],\n",
      "        ...,\n",
      "        [0.4352, 0.4863, 0.4921,  ..., 0.4363, 0.5219, 0.4303],\n",
      "        [0.4071, 0.5572, 0.5083,  ..., 0.4181, 0.5917, 0.5082],\n",
      "        [0.3997, 0.4818, 0.4946,  ..., 0.4359, 0.5713, 0.4978]],\n",
      "       device='cuda:0', grad_fn=<ClampBackward>)\n",
      "tensor([[0.4538, 0.4963, 0.5172,  ..., 0.4601, 0.5745, 0.4860],\n",
      "        [0.4627, 0.4620, 0.4821,  ..., 0.4587, 0.5681, 0.5005],\n",
      "        [0.5068, 0.5224, 0.4338,  ..., 0.4206, 0.5671, 0.4545],\n",
      "        ...,\n",
      "        [0.4329, 0.4854, 0.5022,  ..., 0.4188, 0.5811, 0.4218],\n",
      "        [0.3678, 0.4883, 0.5188,  ..., 0.3971, 0.5731, 0.5204],\n",
      "        [0.4713, 0.4287, 0.4872,  ..., 0.3508, 0.5285, 0.5280]],\n",
      "       device='cuda:0', grad_fn=<ClampBackward>)\n",
      "tensor([[0.4355, 0.5180, 0.4714,  ..., 0.4363, 0.5473, 0.5171],\n",
      "        [0.4338, 0.5383, 0.5123,  ..., 0.4372, 0.6065, 0.5286],\n",
      "        [0.3747, 0.4944, 0.4464,  ..., 0.4547, 0.5129, 0.5425],\n",
      "        ...,\n",
      "        [0.3846, 0.5503, 0.5457,  ..., 0.4188, 0.5690, 0.5314],\n",
      "        [0.4203, 0.4501, 0.4600,  ..., 0.4528, 0.5745, 0.4944],\n",
      "        [0.4135, 0.5354, 0.5175,  ..., 0.4521, 0.5907, 0.4709]],\n",
      "       device='cuda:0', grad_fn=<ClampBackward>)\n",
      "tensor([[0.4402, 0.4281, 0.4924,  ..., 0.4275, 0.5608, 0.4969],\n",
      "        [0.4168, 0.5142, 0.4643,  ..., 0.4790, 0.5446, 0.4674],\n",
      "        [0.4294, 0.4554, 0.4970,  ..., 0.4533, 0.5562, 0.4712],\n",
      "        ...,\n",
      "        [0.4431, 0.4563, 0.4746,  ..., 0.4542, 0.5370, 0.4793],\n",
      "        [0.4622, 0.4648, 0.4836,  ..., 0.4134, 0.5448, 0.4539],\n",
      "        [0.4535, 0.5001, 0.4473,  ..., 0.4063, 0.5832, 0.5434]],\n",
      "       device='cuda:0', grad_fn=<ClampBackward>)\n",
      "tensor([[0.4754, 0.5209, 0.5021,  ..., 0.4367, 0.5860, 0.4687],\n",
      "        [0.3993, 0.5575, 0.4735,  ..., 0.4749, 0.5772, 0.4151],\n",
      "        [0.4573, 0.5076, 0.4820,  ..., 0.4553, 0.5515, 0.4871],\n",
      "        ...,\n",
      "        [0.4002, 0.4696, 0.4826,  ..., 0.4449, 0.5820, 0.4676],\n",
      "        [0.4063, 0.5075, 0.5189,  ..., 0.4140, 0.5632, 0.5036],\n",
      "        [0.4619, 0.5676, 0.4881,  ..., 0.4287, 0.5641, 0.5050]],\n",
      "       device='cuda:0', grad_fn=<ClampBackward>)\n",
      "tensor([[0.4337, 0.4619, 0.5000,  ..., 0.3909, 0.5480, 0.5017],\n",
      "        [0.3404, 0.5361, 0.4924,  ..., 0.4333, 0.5389, 0.4970],\n",
      "        [0.4923, 0.5317, 0.4966,  ..., 0.4838, 0.5613, 0.4436],\n",
      "        ...,\n",
      "        [0.4443, 0.4832, 0.5313,  ..., 0.4642, 0.5933, 0.5146],\n",
      "        [0.4794, 0.5484, 0.4901,  ..., 0.4875, 0.5891, 0.4759],\n",
      "        [0.4507, 0.4801, 0.4878,  ..., 0.3646, 0.5584, 0.4415]],\n",
      "       device='cuda:0', grad_fn=<ClampBackward>)\n",
      "tensor([[0.4362, 0.4581, 0.5277,  ..., 0.4497, 0.5737, 0.5084],\n",
      "        [0.3783, 0.4731, 0.4813,  ..., 0.4327, 0.5446, 0.5474],\n",
      "        [0.3995, 0.5221, 0.5453,  ..., 0.4106, 0.5466, 0.5319],\n",
      "        ...,\n",
      "        [0.4048, 0.5186, 0.4595,  ..., 0.3882, 0.5895, 0.4867],\n",
      "        [0.4306, 0.4536, 0.4656,  ..., 0.4169, 0.5329, 0.5153],\n",
      "        [0.4169, 0.5169, 0.4424,  ..., 0.4787, 0.5575, 0.5145]],\n",
      "       device='cuda:0', grad_fn=<ClampBackward>)\n",
      "tensor([[0.4530, 0.5243, 0.4748,  ..., 0.4350, 0.5776, 0.5203],\n",
      "        [0.4388, 0.5038, 0.4533,  ..., 0.5470, 0.5377, 0.4728],\n",
      "        [0.4688, 0.4794, 0.4944,  ..., 0.4493, 0.6016, 0.4965],\n",
      "        ...,\n",
      "        [0.4748, 0.4848, 0.4723,  ..., 0.3922, 0.5780, 0.5524],\n",
      "        [0.4087, 0.5502, 0.5150,  ..., 0.4888, 0.5651, 0.4824],\n",
      "        [0.4062, 0.4471, 0.4951,  ..., 0.4220, 0.5491, 0.5098]],\n",
      "       device='cuda:0', grad_fn=<ClampBackward>)\n",
      "tensor([[0.4728, 0.4967, 0.5055,  ..., 0.4610, 0.6127, 0.5111],\n",
      "        [0.4203, 0.5020, 0.5028,  ..., 0.4359, 0.5680, 0.4891],\n",
      "        [0.4015, 0.5179, 0.5016,  ..., 0.5606, 0.5251, 0.5110],\n",
      "        ...,\n",
      "        [0.4721, 0.5850, 0.4655,  ..., 0.4227, 0.5761, 0.5077],\n",
      "        [0.3950, 0.5244, 0.4558,  ..., 0.3693, 0.5607, 0.4734],\n",
      "        [0.4335, 0.4235, 0.5299,  ..., 0.4376, 0.5246, 0.4873]],\n",
      "       device='cuda:0', grad_fn=<ClampBackward>)\n"
     ]
    }
   ],
   "source": [
    "x = 15\n",
    "for fake_images in range(x):\n",
    "    z = torch.randn(batch_size, latent_size).to(device)\n",
    "    fake_images = G(z)\n",
    "    D(fake_images)\n",
    "    print(denorm(fake_images))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_generator():\n",
    "    # Generate fake images and calculation loss\n",
    "    z = torch.randn(batch_size, latent_size). to(device)\n",
    "    fake_images = G(z)\n",
    "    labels = torch.ones(batch_size, 1).to(device)\n",
    "    g_loss = criterion(D(fake_images), labels)\n",
    "    \n",
    "    \n",
    "    #Backprop and optimizer\n",
    "    reset_grad()\n",
    "    g_loss.backward()\n",
    "    g_optimizer.step()\n",
    "    return g_loss, fake_images\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os \n",
    "sample_dir = 'samples/'\n",
    "if not os.path.exists(sample_dir):\n",
    "    os.makedirs(sample_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "from torchvision.utils import save_image\n",
    "\n",
    "for images, _ in data_loader:\n",
    "    images = images.reshape(images.size(0), 1, 28, 28)\n",
    "    save_image(denorm(images), \n",
    "                os.path.join(sample_dir, 'real_images.png'), \n",
    "                nrow = 10)\n",
    "    \n",
    "Image(os.path.join(sample_dir, 'real_images.png'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving fake_images-0000.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_vectors = torch.randn(batch_size, latent_size).to(device)\n",
    "\n",
    "def save_fake_images(index):\n",
    "    fake_images = G(sample_vectors)\n",
    "    fake_images = fake_images.reshape(fake_images.size(0), 1, 28, 28)\n",
    "    fake_fname = 'fake_images-{0:0=4d}.png'.format(index)\n",
    "    print('Saving', fake_fname)\n",
    "    save_image(denorm(fake_images), os.path.join(sample_dir, fake_fname), nrow=10)\n",
    "    \n",
    "# Before training\n",
    "save_fake_images(0)\n",
    "Image(os.path.join(sample_dir, 'fake_images-0000.png'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [0/300], Step [200/600], d_loss: 0.0228, g_loss: 5.8571, D(x): 0.99, D(G(z)): 0.01\n",
      "Epoch [0/300], Step [400/600], d_loss: 0.0386, g_loss: 6.7999, D(x): 1.00, D(G(z)): 0.04\n",
      "Epoch [0/300], Step [600/600], d_loss: 0.0252, g_loss: 6.1607, D(x): 0.99, D(G(z)): 0.01\n",
      "Saving fake_images-0001.png\n",
      "Epoch [1/300], Step [200/600], d_loss: 0.0609, g_loss: 7.3206, D(x): 0.99, D(G(z)): 0.05\n",
      "Epoch [1/300], Step [400/600], d_loss: 0.0567, g_loss: 5.9287, D(x): 0.98, D(G(z)): 0.02\n",
      "Epoch [1/300], Step [600/600], d_loss: 0.1775, g_loss: 3.3575, D(x): 0.96, D(G(z)): 0.12\n",
      "Saving fake_images-0002.png\n",
      "Epoch [2/300], Step [200/600], d_loss: 0.2722, g_loss: 5.4140, D(x): 0.94, D(G(z)): 0.07\n",
      "Epoch [2/300], Step [400/600], d_loss: 0.3068, g_loss: 4.6159, D(x): 0.88, D(G(z)): 0.07\n",
      "Epoch [2/300], Step [600/600], d_loss: 0.8468, g_loss: 2.9229, D(x): 0.82, D(G(z)): 0.28\n",
      "Saving fake_images-0003.png\n",
      "Epoch [3/300], Step [200/600], d_loss: 0.2619, g_loss: 4.1435, D(x): 0.95, D(G(z)): 0.14\n",
      "Epoch [3/300], Step [400/600], d_loss: 0.4302, g_loss: 3.0799, D(x): 0.92, D(G(z)): 0.24\n",
      "Epoch [3/300], Step [600/600], d_loss: 0.7757, g_loss: 3.2996, D(x): 0.80, D(G(z)): 0.21\n",
      "Saving fake_images-0004.png\n",
      "Epoch [4/300], Step [200/600], d_loss: 0.1014, g_loss: 5.6965, D(x): 0.95, D(G(z)): 0.02\n",
      "Epoch [4/300], Step [400/600], d_loss: 0.1624, g_loss: 3.8472, D(x): 0.97, D(G(z)): 0.09\n",
      "Epoch [4/300], Step [600/600], d_loss: 0.0859, g_loss: 4.5374, D(x): 0.97, D(G(z)): 0.04\n",
      "Saving fake_images-0005.png\n",
      "Epoch [5/300], Step [200/600], d_loss: 0.1641, g_loss: 8.4967, D(x): 0.95, D(G(z)): 0.04\n",
      "Epoch [5/300], Step [400/600], d_loss: 0.1178, g_loss: 8.5350, D(x): 0.97, D(G(z)): 0.03\n",
      "Epoch [5/300], Step [600/600], d_loss: 0.2753, g_loss: 9.1965, D(x): 0.90, D(G(z)): 0.01\n",
      "Saving fake_images-0006.png\n",
      "Epoch [6/300], Step [200/600], d_loss: 0.2311, g_loss: 5.5287, D(x): 0.96, D(G(z)): 0.11\n",
      "Epoch [6/300], Step [400/600], d_loss: 0.0864, g_loss: 8.3697, D(x): 0.99, D(G(z)): 0.03\n",
      "Epoch [6/300], Step [600/600], d_loss: 0.1501, g_loss: 3.8566, D(x): 0.98, D(G(z)): 0.06\n",
      "Saving fake_images-0007.png\n",
      "Epoch [7/300], Step [200/600], d_loss: 0.1590, g_loss: 4.4218, D(x): 0.96, D(G(z)): 0.05\n",
      "Epoch [7/300], Step [400/600], d_loss: 0.2103, g_loss: 4.3404, D(x): 0.92, D(G(z)): 0.05\n",
      "Epoch [7/300], Step [600/600], d_loss: 0.3058, g_loss: 4.4339, D(x): 1.00, D(G(z)): 0.19\n",
      "Saving fake_images-0008.png\n",
      "Epoch [8/300], Step [200/600], d_loss: 0.0708, g_loss: 7.2483, D(x): 0.97, D(G(z)): 0.01\n",
      "Epoch [8/300], Step [400/600], d_loss: 0.0760, g_loss: 5.2349, D(x): 0.98, D(G(z)): 0.04\n",
      "Epoch [8/300], Step [600/600], d_loss: 0.1959, g_loss: 7.7748, D(x): 0.94, D(G(z)): 0.01\n",
      "Saving fake_images-0009.png\n",
      "Epoch [9/300], Step [200/600], d_loss: 0.1028, g_loss: 6.3938, D(x): 0.96, D(G(z)): 0.01\n",
      "Epoch [9/300], Step [400/600], d_loss: 0.2284, g_loss: 4.2995, D(x): 0.94, D(G(z)): 0.05\n",
      "Epoch [9/300], Step [600/600], d_loss: 0.1876, g_loss: 5.3787, D(x): 0.93, D(G(z)): 0.03\n",
      "Saving fake_images-0010.png\n",
      "Epoch [10/300], Step [200/600], d_loss: 0.1790, g_loss: 4.3551, D(x): 0.97, D(G(z)): 0.10\n",
      "Epoch [10/300], Step [400/600], d_loss: 0.2135, g_loss: 5.7993, D(x): 0.96, D(G(z)): 0.10\n",
      "Epoch [10/300], Step [600/600], d_loss: 0.1661, g_loss: 3.6510, D(x): 0.97, D(G(z)): 0.10\n",
      "Saving fake_images-0011.png\n",
      "Epoch [11/300], Step [200/600], d_loss: 0.1865, g_loss: 6.6241, D(x): 0.90, D(G(z)): 0.01\n",
      "Epoch [11/300], Step [400/600], d_loss: 0.2326, g_loss: 4.5377, D(x): 0.98, D(G(z)): 0.15\n",
      "Epoch [11/300], Step [600/600], d_loss: 0.1904, g_loss: 4.4308, D(x): 0.96, D(G(z)): 0.08\n",
      "Saving fake_images-0012.png\n",
      "Epoch [12/300], Step [200/600], d_loss: 0.1529, g_loss: 4.2953, D(x): 0.95, D(G(z)): 0.05\n",
      "Epoch [12/300], Step [400/600], d_loss: 0.1465, g_loss: 3.8723, D(x): 0.96, D(G(z)): 0.06\n",
      "Epoch [12/300], Step [600/600], d_loss: 0.2642, g_loss: 5.7695, D(x): 0.99, D(G(z)): 0.16\n",
      "Saving fake_images-0013.png\n",
      "Epoch [13/300], Step [200/600], d_loss: 0.2609, g_loss: 4.5090, D(x): 0.96, D(G(z)): 0.13\n",
      "Epoch [13/300], Step [400/600], d_loss: 0.3452, g_loss: 4.4516, D(x): 0.88, D(G(z)): 0.03\n",
      "Epoch [13/300], Step [600/600], d_loss: 0.0547, g_loss: 5.8991, D(x): 0.97, D(G(z)): 0.02\n",
      "Saving fake_images-0014.png\n",
      "Epoch [14/300], Step [200/600], d_loss: 0.3490, g_loss: 4.7998, D(x): 0.91, D(G(z)): 0.08\n",
      "Epoch [14/300], Step [400/600], d_loss: 0.4217, g_loss: 4.2197, D(x): 0.89, D(G(z)): 0.03\n",
      "Epoch [14/300], Step [600/600], d_loss: 0.1644, g_loss: 3.9562, D(x): 0.99, D(G(z)): 0.12\n",
      "Saving fake_images-0015.png\n",
      "Epoch [15/300], Step [200/600], d_loss: 0.2820, g_loss: 3.2618, D(x): 0.99, D(G(z)): 0.20\n",
      "Epoch [15/300], Step [400/600], d_loss: 0.1063, g_loss: 6.7059, D(x): 0.96, D(G(z)): 0.03\n",
      "Epoch [15/300], Step [600/600], d_loss: 0.1603, g_loss: 5.0744, D(x): 0.98, D(G(z)): 0.09\n",
      "Saving fake_images-0016.png\n",
      "Epoch [16/300], Step [200/600], d_loss: 0.2776, g_loss: 6.3882, D(x): 0.89, D(G(z)): 0.03\n",
      "Epoch [16/300], Step [400/600], d_loss: 0.1490, g_loss: 5.0080, D(x): 0.98, D(G(z)): 0.11\n",
      "Epoch [16/300], Step [600/600], d_loss: 0.3828, g_loss: 2.8217, D(x): 0.97, D(G(z)): 0.23\n",
      "Saving fake_images-0017.png\n",
      "Epoch [17/300], Step [200/600], d_loss: 0.1631, g_loss: 5.8159, D(x): 0.97, D(G(z)): 0.10\n",
      "Epoch [17/300], Step [400/600], d_loss: 0.2696, g_loss: 4.7886, D(x): 0.92, D(G(z)): 0.04\n",
      "Epoch [17/300], Step [600/600], d_loss: 0.3142, g_loss: 4.7201, D(x): 0.95, D(G(z)): 0.08\n",
      "Saving fake_images-0018.png\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
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     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Epoch [282/300], Step [200/600], d_loss: 0.9287, g_loss: 1.4110, D(x): 0.69, D(G(z)): 0.31\n",
      "Epoch [282/300], Step [400/600], d_loss: 0.7806, g_loss: 1.8556, D(x): 0.74, D(G(z)): 0.28\n",
      "Epoch [282/300], Step [600/600], d_loss: 0.9192, g_loss: 1.6260, D(x): 0.73, D(G(z)): 0.34\n",
      "Saving fake_images-0283.png\n",
      "Epoch [283/300], Step [200/600], d_loss: 0.9887, g_loss: 0.9249, D(x): 0.73, D(G(z)): 0.38\n",
      "Epoch [283/300], Step [400/600], d_loss: 0.7227, g_loss: 1.4869, D(x): 0.75, D(G(z)): 0.26\n",
      "Epoch [283/300], Step [600/600], d_loss: 0.9234, g_loss: 1.5503, D(x): 0.71, D(G(z)): 0.30\n",
      "Saving fake_images-0284.png\n",
      "Epoch [284/300], Step [200/600], d_loss: 0.9666, g_loss: 1.4878, D(x): 0.70, D(G(z)): 0.36\n",
      "Epoch [284/300], Step [400/600], d_loss: 1.0592, g_loss: 1.1632, D(x): 0.74, D(G(z)): 0.38\n",
      "Epoch [284/300], Step [600/600], d_loss: 0.9011, g_loss: 1.9537, D(x): 0.80, D(G(z)): 0.40\n",
      "Saving fake_images-0285.png\n",
      "Epoch [285/300], Step [200/600], d_loss: 1.0902, g_loss: 1.4657, D(x): 0.71, D(G(z)): 0.39\n",
      "Epoch [285/300], Step [400/600], d_loss: 0.9652, g_loss: 1.2836, D(x): 0.73, D(G(z)): 0.36\n",
      "Epoch [285/300], Step [600/600], d_loss: 1.0106, g_loss: 1.5480, D(x): 0.78, D(G(z)): 0.42\n",
      "Saving fake_images-0286.png\n",
      "Epoch [286/300], Step [200/600], d_loss: 1.0213, g_loss: 1.6626, D(x): 0.67, D(G(z)): 0.32\n",
      "Epoch [286/300], Step [400/600], d_loss: 0.7184, g_loss: 1.5171, D(x): 0.75, D(G(z)): 0.24\n",
      "Epoch [286/300], Step [600/600], d_loss: 0.7705, g_loss: 1.6160, D(x): 0.71, D(G(z)): 0.22\n",
      "Saving fake_images-0287.png\n",
      "Epoch [287/300], Step [200/600], d_loss: 0.7865, g_loss: 1.5070, D(x): 0.80, D(G(z)): 0.34\n",
      "Epoch [287/300], Step [400/600], d_loss: 0.7390, g_loss: 1.2329, D(x): 0.75, D(G(z)): 0.27\n",
      "Epoch [287/300], Step [600/600], d_loss: 0.9862, g_loss: 1.5731, D(x): 0.67, D(G(z)): 0.33\n",
      "Saving fake_images-0288.png\n",
      "Epoch [288/300], Step [200/600], d_loss: 0.8815, g_loss: 1.4527, D(x): 0.70, D(G(z)): 0.28\n",
      "Epoch [288/300], Step [400/600], d_loss: 1.0311, g_loss: 1.7047, D(x): 0.58, D(G(z)): 0.22\n",
      "Epoch [288/300], Step [600/600], d_loss: 0.9186, g_loss: 1.3455, D(x): 0.69, D(G(z)): 0.28\n",
      "Saving fake_images-0289.png\n",
      "Epoch [289/300], Step [200/600], d_loss: 0.7712, g_loss: 1.6661, D(x): 0.72, D(G(z)): 0.25\n",
      "Epoch [289/300], Step [400/600], d_loss: 0.7967, g_loss: 1.6500, D(x): 0.70, D(G(z)): 0.24\n",
      "Epoch [289/300], Step [600/600], d_loss: 0.7777, g_loss: 1.5845, D(x): 0.76, D(G(z)): 0.29\n",
      "Saving fake_images-0290.png\n",
      "Epoch [290/300], Step [200/600], d_loss: 1.0394, g_loss: 1.4184, D(x): 0.69, D(G(z)): 0.35\n",
      "Epoch [290/300], Step [400/600], d_loss: 0.8125, g_loss: 1.6421, D(x): 0.71, D(G(z)): 0.26\n",
      "Epoch [290/300], Step [600/600], d_loss: 0.8867, g_loss: 1.7773, D(x): 0.72, D(G(z)): 0.29\n",
      "Saving fake_images-0291.png\n",
      "Epoch [291/300], Step [200/600], d_loss: 0.9016, g_loss: 1.6541, D(x): 0.73, D(G(z)): 0.32\n",
      "Epoch [291/300], Step [400/600], d_loss: 0.9151, g_loss: 1.7363, D(x): 0.76, D(G(z)): 0.36\n",
      "Epoch [291/300], Step [600/600], d_loss: 0.9838, g_loss: 1.0678, D(x): 0.78, D(G(z)): 0.42\n",
      "Saving fake_images-0292.png\n",
      "Epoch [292/300], Step [200/600], d_loss: 0.8548, g_loss: 1.5685, D(x): 0.72, D(G(z)): 0.28\n",
      "Epoch [292/300], Step [400/600], d_loss: 0.9604, g_loss: 1.7927, D(x): 0.74, D(G(z)): 0.35\n",
      "Epoch [292/300], Step [600/600], d_loss: 0.8812, g_loss: 1.8810, D(x): 0.71, D(G(z)): 0.26\n",
      "Saving fake_images-0293.png\n",
      "Epoch [293/300], Step [200/600], d_loss: 1.0281, g_loss: 1.7363, D(x): 0.71, D(G(z)): 0.35\n",
      "Epoch [293/300], Step [400/600], d_loss: 0.7550, g_loss: 1.8053, D(x): 0.77, D(G(z)): 0.29\n",
      "Epoch [293/300], Step [600/600], d_loss: 0.9735, g_loss: 1.7352, D(x): 0.59, D(G(z)): 0.21\n",
      "Saving fake_images-0294.png\n",
      "Epoch [294/300], Step [200/600], d_loss: 0.9523, g_loss: 1.4644, D(x): 0.63, D(G(z)): 0.26\n",
      "Epoch [294/300], Step [400/600], d_loss: 0.8317, g_loss: 1.3456, D(x): 0.76, D(G(z)): 0.33\n",
      "Epoch [294/300], Step [600/600], d_loss: 0.8442, g_loss: 1.6301, D(x): 0.67, D(G(z)): 0.22\n",
      "Saving fake_images-0295.png\n",
      "Epoch [295/300], Step [200/600], d_loss: 0.9952, g_loss: 1.2927, D(x): 0.76, D(G(z)): 0.37\n",
      "Epoch [295/300], Step [400/600], d_loss: 1.0066, g_loss: 2.1304, D(x): 0.55, D(G(z)): 0.17\n",
      "Epoch [295/300], Step [600/600], d_loss: 0.8581, g_loss: 1.6101, D(x): 0.67, D(G(z)): 0.24\n",
      "Saving fake_images-0296.png\n",
      "Epoch [296/300], Step [200/600], d_loss: 0.7006, g_loss: 1.6998, D(x): 0.75, D(G(z)): 0.23\n",
      "Epoch [296/300], Step [400/600], d_loss: 0.9000, g_loss: 1.8352, D(x): 0.66, D(G(z)): 0.23\n",
      "Epoch [296/300], Step [600/600], d_loss: 0.9132, g_loss: 1.4701, D(x): 0.77, D(G(z)): 0.34\n",
      "Saving fake_images-0297.png\n",
      "Epoch [297/300], Step [200/600], d_loss: 0.9515, g_loss: 1.7072, D(x): 0.74, D(G(z)): 0.36\n",
      "Epoch [297/300], Step [400/600], d_loss: 0.9835, g_loss: 1.6657, D(x): 0.79, D(G(z)): 0.41\n",
      "Epoch [297/300], Step [600/600], d_loss: 0.8493, g_loss: 1.6953, D(x): 0.72, D(G(z)): 0.29\n",
      "Saving fake_images-0298.png\n",
      "Epoch [298/300], Step [200/600], d_loss: 0.8331, g_loss: 1.5462, D(x): 0.78, D(G(z)): 0.34\n",
      "Epoch [298/300], Step [400/600], d_loss: 0.9145, g_loss: 1.6755, D(x): 0.66, D(G(z)): 0.26\n",
      "Epoch [298/300], Step [600/600], d_loss: 0.9313, g_loss: 1.7691, D(x): 0.65, D(G(z)): 0.22\n",
      "Saving fake_images-0299.png\n",
      "Epoch [299/300], Step [200/600], d_loss: 1.0005, g_loss: 1.6205, D(x): 0.71, D(G(z)): 0.31\n",
      "Epoch [299/300], Step [400/600], d_loss: 1.1174, g_loss: 1.4127, D(x): 0.62, D(G(z)): 0.27\n",
      "Epoch [299/300], Step [600/600], d_loss: 0.9386, g_loss: 1.4634, D(x): 0.80, D(G(z)): 0.39\n",
      "Saving fake_images-0300.png\n",
      "Wall time: 1h 38min 53s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "num_epochs = 300\n",
    "total_step = len(data_loader)\n",
    "d_losses, g_losses, real_scores, fake_scores = [], [], [], []\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    for i, (images, _) in enumerate(data_loader):\n",
    "        # Load a batch & transform to vectors\n",
    "        images = images.reshape(batch_size, -1).to(device)\n",
    "        \n",
    "        # Train the discriminator and generator\n",
    "        d_loss, real_score, fake_score = train_discriminator(images)\n",
    "        g_loss, fake_images = train_generator()\n",
    "        \n",
    "        # Inspect the losses\n",
    "        if (i+1) % 200 == 0:\n",
    "            d_losses.append(d_loss.item())\n",
    "            g_losses.append(g_loss.item())\n",
    "            real_scores.append(real_score.mean().item())\n",
    "            fake_scores.append(fake_score.mean().item())\n",
    "            print('Epoch [{}/{}], Step [{}/{}], d_loss: {:.4f}, g_loss: {:.4f}, D(x): {:.2f}, D(G(z)): {:.2f}' \n",
    "                  .format(epoch, num_epochs, i+1, total_step, d_loss.item(), g_loss.item(), \n",
    "                          real_score.mean().item(), fake_score.mean().item()))\n",
    "        \n",
    "    # Sample and save images\n",
    "    save_fake_images(epoch+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the model checkpoints \n",
    "torch.save(G.state_dict(), 'G.ckpt')\n",
    "torch.save(D.state_dict(), 'D.ckpt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<a href='GAN_Output.avi' target='_blank'>GAN_Output.avi</a><br>"
      ],
      "text/plain": [
       "D:\\GAN-Internship-Project\\GAN_Output.avi"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import cv2\n",
    "import os\n",
    "from IPython.display import FileLink\n",
    "\n",
    "video_avi = 'GAN_Output.avi'\n",
    "\n",
    "files = [os.path.join(sample_dir, f) for f in os.listdir(sample_dir) if 'fake_images' in f]\n",
    "files.sort()\n",
    "\n",
    "out = cv2.VideoWriter(video_avi,cv2.VideoWriter_fourcc(*'MP4V'), 8, (302,302))\n",
    "[out.write(cv2.imread(fname)) for fname in files]\n",
    "out.release()\n",
    "FileLink('GAN_Output.avi')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Losses')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(d_losses, '-')\n",
    "plt.plot(g_losses, '-')\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('loss')\n",
    "plt.legend(['Discriminator', 'Generator'])\n",
    "plt.title('Losses')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(real_scores, '-')\n",
    "plt.plot(fake_scores, '-')\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('score')\n",
    "plt.legend(['Real Score', 'Fake score'])\n",
    "plt.title('Scores');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install jovian --upgrade -q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import jovian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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